Maximising ecological value and assessing land suitability for sustainable grassland management in Asia’s largest tropical grassland, Western India

Grasslands are crucial ecosystems that provide numerous ecological services and support biodiversity conservation. Grasslands undergo significant threats from both anthropogenic and natural sources, compromising their ability to maintain biodiversity, ecosystem services, and human well-being. However, grasslands are frequently ignored in sustainable development objectives. Adequate knowledge of how grassland degradation affects ecosystem services is essential for sustainable management and grassland ecological restoration. The Kachchh region in western India harbours a unique grassland ecosystem known as the Banni grassland, which once became the finest grassland in Asia. However, undesirable anthropogenic interventions have accelerated its degradation. This research paper aims to assess the suitability of different land areas in Banni for sustainable grassland restoration, considering ecological value as a primary criterion. In the present research, land suitability for grassland management was assessed using a geographical information system (GIS)-based multi criteria evolution (MCE) method with satellite data and the analytic hierarchy process (AHP). The ground truthing of the soil samples was carried out alongside. Slope, rainfall, infiltration rate, LULC, geomorphology, soil texture, soil organic carbon, water holding capacity, SAR, CEC, pH, EC, and soil nutrients were among the criteria used. The weights for each criterion were calculated using a pairwise comparison matrix, and the scores were allocated to sub criteria based on field work, expert opinions, and a literature review. The proposed method can be very useful for evaluating the state of the land and can help with the best possible planning for grassland development and conservation. Banni grassland has the potential to be developed into a critical zone observatory (CZO) in the future, and the present study, with further inputs, holds promise for furthering the cause of its sustainable management. Overall, this study underscores the importance of assessing land suitability for sustainable grassland management and highlights the potential for maximising the ecological value of grasslands in western India and beyond.

environment.Furthermore, the primary hypothesis of this study is aimed not only at assisting in the identification of lands suitable for grassland development but also at assisting in the sustainable management of territories by assessing the characteristics of soils, which are the most significant and vulnerable components of arid and semiarid terrestrial ecosystems.

Study area
The Banni plains are among the most enigmatic geological features in the Kachchh region, Western India (23° 19′ to 23 ° 52′ N latitude and 68° 56′ to 70 ° 32′ E longitude).It is a highly semiarid fragile ecosystem with slightly undulating and rough terrain.Banni plains are predominantly flat saline land with several shallow depressions, which act as seasonal wetlands after monsoons, and during winters, they convert into sedge mixed grasslands, an ideal dual ecosystem.The yearly rainfall in the area is unpredictable and low (mean 288 mm), with a coefficient of variation ranging from 60 to 80%.The average annual rainfall is approximately 317 mm 13,43 .The monsoon season, which lasts from June to September, brings more than 80% of the annual precipitation.The average temperature of Banni varies from 49 °C in the summer (May-June) to 10 °C in the winter (January-February) 13 .Banni is dominated by low-growing forbs and graminoids, which are mostly halophiles (salt tolerant), as well as scattered tree cover and scrub.The tree cover consists of Salvadora spp.and the invasive Prosopisjuliflora while Cressacretica, Cyperus spp., Sporobolus, Dichanthium, and Aristida are the dominant species found in the area 44 .The soils of Banni are saline sodic, posing a threat to cultivation of any crop except the halophytic grasses and sturdy plant species 43 .The soils belong majorly to cambic arenosols in high level mudflats, whereas in low level mudflats solanchaks are dominant 43 .Geologically, the plains of Banni are tectonically raised mudflats of the earlier Rann surface, with a vast flat terrain.Itis highly vegetated with thorny shrubs and grasses and extends from mainland Kachchh in the south to Pachham Island and the Great Rann in the north 43,45 .It is a distinctive geomorphic surface of the Great Rann that is located at the greatest altitude and is, as a result, entirely devoid of current marine influence.Geologically, the Banni Plains are the deposition sites for Late Pleistocene to Holocene sediments from the northern river systems, rivers of various uplifts of the Kachchh basin and marine sediments of the Arabian Sea from the west [46][47][48] .The geomorphology reveals that the Banni Plains are a large palaeo mud flat with an elevation of 2-12 m above mean sea level.There are many ephemeral streams, viz.Nara, Chari, Kadrai, Bhukhi, Nirona, Kaila, Pur, Kasvali, Lotia and Khirsara flow across the Kachchh mainland fault (KMF) and into the Banni Plains from the south (Fig. 1).These ephemeral rivers deposited alluvial fan sediments, which cover www.nature.com/scientificreports/ the southern end of the Banni Plains.The Banni Plains are constrained to the south by the Kachchh mainland fault and to the north by the Banni fault 49,50 .

Experimental design and data collection
To achieve this goal, field data and soil surveys were collected from the dry grasslands of Banni.The investigation was carried out during the dry season at a total of forty-five grassland locations.The coordinates and altitudes of the research areas were recorded on the spot using a handheld Garmin 72H global positioning system.The bioclimate of the survey sites varies from hyperarid to arid.The soil sampling depth was fixed to 50 cm.ArcGIS 10.7 was utilised to a generate 10 × 10 km (Fig. 2) remote sensing grid and transects at each research location with a subgrid of approximately 5 × 5 km.The research area's elevations were considered while choosing the sampling locations to minimise the potential effects of different microclimates driven by tectonics, aspect, and slope.Soil samples were sun-dried from the designated sampling locations before testing.Using a 2 mm filter, the samples were homogenised to remove any large soil particles or roots.Next, the physicochemical content of the soils was examined.The standardised standard protocols, as described by Alef and Nannipieri, were followed for the analysis of the physicochemical characteristics of the soils 51 .Using a digital electrical conductivity (EC) meter and a glass electrode, the samples were examined for electrical conductivity and pH in a 1:2 (w/v) soil solution 52 and the water holding capacity was obtained using the gravimetric method 53 .Keen's cup method 53 was applied for the determination of soil bulk density.The organic carbon (SOC) in the soil was determined using the titrimetric determination method of Walkley and Black, 1934 54 .The soil texture was determined using the soil sieve method according to the USDA soil texture classification.All analyses were carried out in triplicate.Moreover, soils were examined for specific micronutrients, including iron (Fe), zinc (Zn), manganese (Mn), and copper (Cu), in accordance with earlier research describing the presence of various micronutrients and their mechanisms of action in diverse grasslands.An atomic absorption spectrophotometer (AAS) was used for micronutrient analysis.Soil extracts were combined with DTPA (diethylene tri amine pentaacetic acid) at a 1:2 ratio for the digestion of trace metals.AAS was used to evaluate the extracts at a flame temperature of 2950 °C.
The equation below was used to calculate all of the minor nutrients.
Where Nc is the nutrient concentration in ppm, Sr is the sample reading and Br is the blank reading of the sample.For available phosphorus, Olsen's method for neutral alkaline soil 55 was adopted.The total P in the soil samples was extracted by a mixture of concentrated sulfuric acid, hydrofluoric acid and hydrogen peroxide 56 , and the P concentration of the extract was determined using the same method as for available P. Potassium and sodium concentrations were determined by the flame photometer method 57 .We used the alkaline KMnO 4 method with auto Kjeldahl to determine the available nitrogen in the study area 58 .A double-ring infiltrometer was used to measure the rate of soil infiltration (the measurements were made in accordance with the ASTM D3385-03 standard test methodology) 59  www.nature.com/scientificreports/ that are 30 and 60 cm in diameter, respectively.They were placed on the surface of the earth and securely attached 10 cm below the surface.As long as there was no more water infiltration, the tests were conducted.Eq. ( 2) was used to estimate infiltration rates: Where a represents the rate of infiltration in millimeters, which is calculated as the volume of water delivered to the inner ring, and b represents the interval of time (in minutes) between two successive readings.The Ca ++ and Mg ++ contents in the soils were estimated through the standard versenate method (EDTA complexometric titration) from water and ammonium acetate extracts at a 1:5 ratio.Na and K were analysed through flame photometry from water and ammonium acetate extracts at 1:5 ratios 60 .The values of the water extracts were subtracted from the values of the ammonium acetate extracts to calculate the corresponding net exchangeable values.The results were subjected to calculations of the SAR (sodium adsorption ratio) and CEC (cation exchange capacity).The SAR and CEC were calculated through the following equations: Additionally, Sentinel-2 satellite data with a spatial resolution of 30 m were utilised to generate the land use and land cover map, geomorphology, and land surface temperature.Slope maps were produced using DEM data from ASTER at a resolution of 1 arc second.The rainfall data were sourced from the Indian Meteorological Department.We excluded geological data because study area a has uniform features known as the Banni plains.Table 1 shows the spatial characteristics of the data employed in the study.To compare all the parameters with further field measurements, all the parameters were recorded.The geographic distribution may be determined by comparing the parameters from several field measurements.These well-defined sites are crucial for comprehending the spatial relationships and insight mechanisms of the processes affecting the suitability and dynamics of grasslands.Additionally, interpolation of these factors will reveal details on the spatial variations in the Banni Plain.Maps of the collected data were generated using inverse distance weighted (IDW) techniques.The IDW interpolation clearly assumes that nearby objects are more similar than those farther away 61 .The IDW is given by: The adjustment k j in this formula ensures that the weights total up to 1. Z i is the value of an established point.The distance between known points is d ij .The unknown point is Z j, andn is a user-selectable exponent.
Using the Arc GIS 10.7, Erdas 9.2 and Idrisi Selva image processing techniques, the satellite imagery bands were combined, fused, and layered.A traditional coordination system was applied to all of these factors, and projection and weighted overlay analysis (WOA) were performed.

Multi criteria decision analysis using GIS techniques
For identifying grassland suitability zones, multi criteria decision analysis using the analytical hierarchical process (AHP) is the most widely used and well-known GIS-based method.The integration of all theme levels is facilitated by this method.Twenty distinct theme levels were considered for this investigation.The dynamics of the area's grasslands are thought to be governed by these 20 thematic levels.These influencing factors are weighted according to their interactions with expert evaluations, terrain, climate, and edaphic characteristics.A layer that has a significant influence on the suitability of grasslands is indicated by a high weight value, whereas a layer that (5) has little impact is indicated by a low weight parameter.The weights allocated to each parameter (Fig. 3) were determined by applying Saaty's relative importance value scale (1-9).Furthermore, the weights were assigned by taking into account the review of past research and field experience.The Saaty's scale of relative importance suggests that a value of 9 indicates extreme significance; 8, very strong; 7, very to extreme importance; 6, strong plus; 5, strong importance; 4, moderate plus; 3, moderate importance; 2, weak importance; and 1, equal importance.Thematic layers are classified according to their relevance, and weights are allocated to them accordingly.To compare all of the theme layers with one another, a pairwise comparison matrix (Table 2) was used.To assign weights, thematic layer subclasses were reclassified using the natural breaks classification method of the GIS platform.The subclasses of each theme layer were given a score between 0 and 9 according to how each affected the suitability and productivity of the grasslands 28 .The ranked and weighted thematic layers are shown in Table 3.The following procedures were used to calculate the  www.nature.com/scientificreports/consistency ratio (CR): (1) the principal eigenvalue (ʎ) was computed by the eigenvector technique (Table 4), and (2) the consistency index (CI) was calculated from the equation given below: www.nature.com/scientificreports/ Where n is the number of factors used in the analysis.CI = (20−20)/(20−1) = 0.The consistency ratio is defined as CR = CI/RCI, where RCI = random consistency index value, whose values were obtained from Saaty's standard 62 (Table 5).CR = 0/1.63= 0.According to Saaty 62 , a CR of 0.10 or less is sufficient to continue the analysis.If the consistency value is greater than 0.10, the judgement must be reviewed to identify the root reasons for the inconsistency and to make the necessary corrections.If the CR is 0, then the pairwise comparison has complete consistency.The judgement matrix is relatively consistent because the threshold value is not higher than 0.1.The consistency ratios of all the thematic layers are shown in Table 4.
To generate a map of the grassland suitability of the Banni Plain, all twenty thematic layers were integrated with the weighted overlay analysis method in the GIS platform using Eq. ( 9): Where GSZ = Grassland suitability zones, X-represents the weight of the thematic layers, and Y-represents the rank of the thematic layer sub-class.A (A = 1, 2, 3, ……, X) represents the thematic map, and B (B = 1, 2, 3, ……, Y) represents the thematic map class.

Statistical analysis
The sets of data derived from the aforementioned investigations were subjected to a number of statistical tests to determine whether the variations in soil physicochemical characteristics and climatic, topographic, and nutrient concentrations across the Banni Grassland were significant.First, the Shapiro-Wilk normality (p ≤ 0.05) test was performed on the datasets to ensure that they were not normally distributed.Additionally, the Kruskal-Wallis H test was selected as the non-parametric test that could be used on the data to identify significant differences across the different divisions.Statistical analysis was conducted using the following statistical software: IBM SPSS version 26, SAS version 9.3, Microsoft Excel version 7, and Origin Pro 2022.

Soil nitrogen
Nitrogen is a crucial nutrient for plants since it is one of the major nutrients required in high proportions for plant metabolism and development and is the most limiting factor for grassland productivity 63,64 .Atmospheric nitrogen is the principal source of nitrogen in the soil.Before it can be used in the soil, it must be converted from its natural state as N 2 in the atmosphere.Although nitrogen is naturally present in the soil in organic forms as residues of plants and animals, it is not readily available to plants and must first undergo transformation by microbes for them to use it 65 .The inorganic forms NH 4 + and NO 3 − (sometimes referred to as mineral nitrogen) make up the majority of the nitrogen that is accessible to plants.Nitrogen is often sluggishly released from soil minerals and only provides a small amount of nitrogen to the soil.The range of soil nitrogen (N) in the research region is 50.53 to 610.49 mg/kg (Fig. 5A), with the bulk falling in the very low category (50.53 to 129.58 mg/ kg) and spanning an area of approximately 2356.20 sq.km, or approximately 90.19% of the land area.(Fig. 4b).

Soil phosphorus
Second only to nitrogen, phosphorus (P) is a major limiting nutrient in agriculture and is removed from arable and grassland soils by crop and grass withdrawals and erosion 66 .The main function of phosphorus in plants is to store and transfer energy produced during photosynthesis for use in growth and imination.Numerous processes include P cycling in the soil.Adequate P promotes root development, winter hardiness, tillering, and maturity.The soil phosphate concentration is a good indicator of P cycling in soils and a crop's response to P fertiliser.The rate of P mineralisation from the decomposition of organic matter is influenced by site and climate factors such as salinity, moisture, and soil aeration.In well-aerated soils (where oxygen concentrations are greater), phosphorus is released more quickly, whereas in saturated, wet soils, it is released much more slowly 63 .The soil P in the study region ranged from 1.57 to 56.77 kg/hac (Fig. 5B), with the majority falling between 15.37 and 29.17 kg/hac (low category) and spanning an area of approximately 1052.59 sq.km, or approximately 40.29% of the land area.(Fig. 4b).In contrast to the 740.33 sq.km with very low P (28.34%) and 704.04 sq.km with a moderate P concentration (26.95%),only 115.17 sq.km (4.40%) of the region is coated by high P concentrations.

Soil potassium
Together with nitrogen and phosphorus, potassium (K) is one of the three major plant macronutrients that are consumed relatively significantly by plants from soil.Potassium increases grassland productivity and improves product quality 63 .Additionally, potassium aids plants in overcoming environmental challenges such as disease, insect attacks, and cold and drought stress.It facilitates the development of a strong and healthy root system and increases the effectiveness of nutrient uptake and usage, particularly for nitrogen.Furthermore, cattle nutrition depends on potassium.Potassium (K) is an essential nutrient for plant growth.N, P, and K all have impacts on grassland production, with N and P acting as the major limiting factors and K acting as the secondary limiting factor.Among the functional groups, P is the most limiting factor, followed by N and P for grasses, P for forbs, and all three for shrubs 67 .The range of soil K concentrations in the study area is 0.11-1.94mg/L (Fig. 5C).Generally, the K content was lower in all the samples.However, the K was moderate to fairly acceptable in some patches of the research site.The K content in the soil, however, suggests that the available K in the soil is low when compared to the approved limits.Approximately 45.15% (1179.45sq.km) of the land had available K contents in the range of 0.11 to 0.56 (mg/ltr), 47.30% (1235.55sq.km) had available K contents in the range of 0.56 to 1.02 (mg/ltr), 6.23% (162.98 sq.km) had available K contents in the range of 1.02 to 1.48 (mg/ltr), and 1.30% (34.10 sq.km) had available K contents in the range of 1.48 to 1.94 (mg/ltr).

Iron (Fe)
Minute amounts of micronutrients, sometimes referred to as trace minerals, are needed by plants and animals.
The main heavy metals include copper (Cu), zinc (Zn), manganese (Mn), and iron (Fe).This, however, does not imply that their contribution is insignificant.Their absence, for instance, might result in severe crop productivity issues in forages and health issues in cattle 68 .Fe is necessary for the production of chlorophyll and photosynthesis in plants.The distribution of plant species in natural ecosystems is determined by soil iron availability, which also restricts crop output and nutrient quality.The Fe concentrations ranged from 1.92 to 68.78 ppm (Fig. 5D) in the study region, with the majority ranging between 1.92 and 18. 64

Manganese (Mn)
In addition to assisting metabolic processes in several plant cell compartments, manganese (Mn) is an essential element for plant growth and development 69 .It also plays a significant role in the activation of various enzymes involved in the citric acid cycle, oxidation processes, carboxylation, carbohydrate metabolism, and phosphorus reactions 70 .In sandy soils with a high pH and a high level of organic matter, manganese reactions are most likely to occur 68 .Mn levels ranged from 2.27 to 34.71 ppm in the study region (Fig. 5E), with the majority ranging between 2. 27

Zinc (Zn)
Zinc is a crucial micronutrient for humans, animals, and plants.Zn plays a significant role in numerous enzymes that catalyse metabolic processes in plants.Additionally, zinc is important for photosynthesis, cell membrane integrity, protein synthesis, pollen development, and disease resistance in plants and increases the levels of antioxidant enzymes and chlorophyll in plant tissues 71 .Zn levels in the study region ranged from 0.24 to 3.89 ppm (Fig. 5F), with the majority occurring between 0.24 and 1.15 ppm and between 1. 15

Copper (Cu)
Copper plays a vital role in several plant functions.It is essential for photosynthesis, enzyme activity, and the formation of lignin, a component of plant cell walls.Copper also aids in the uptake and utilisation of iron, promoting overall plant growth and development 70 .However, excessive copper in soil can be toxic to plants, causing damage to roots and inhibiting their ability to absorb water and nutrients.The Cu concentrations in the research area varied from 0.30 to 3.34 ppm (Fig. 5G), although most were between 1.06 and 1. 82

Soil pH
One of the key elements affecting the overall composition of grassland ecosystems is the pH of the soil 72 .Soil pH has an impact on nutrient availability, plant growth, and crop output.The minerals that make up soil have a major role in determining how it responds.The availability of nutrients, phytotoxicity, and crop compatibility may all be affected by pH.The optimal pH range for the majority of plants is between 5.5 and 7.0.The alkaline pH is greater than 7, whereas the acidic pH is less than 7 73 .The parent material, organic matter content, and inorganic matter content are the key factors affecting grassland soil pH.For instance, quartz and feldspar are the primary sources of silt and have direct and indirect impacts on H + ion accumulation, lowering the pH of grassland soils.Additionally, organic matter releases H + ions into the soil and can lower the pH because it includes numerous acid functional groups that are a source of H + ions in the soil.As the soil pH increases, the abundance of grassland species also decreases 72 .The soil samples from the research area had a pH that varied from 7.23 to 8.7 (Fig. 5H), with an average pH of approximately www.nature.com/scientificreports/

Soil texture
Since soil texture is a crucial factor in the laws governing grassland ecosystems 74 , determining soil texture functioning zones requires a precise methodology.Numerous physical and chemical properties are directly influenced by soil texture, and these properties in turn influence soil fertility and productivity.Soil texture affects drainage and water retention in agriculture, which affects crop selection and irrigation methods 75 .Soil texture impacts biodiversity and plant communities in ecological settings, influencing habitat suitability and supporting several ecosystem processes, including grassland stability 74 .The most common soil textures in the study region are silt loam and clay loam, which span an area of approximately 1170.72 sq.km (44.82%) and 599.10 sq.km (22.93%), respectively (Fig. 5I).Similarly, loam, silty clay, sandy loam, loamy sand and silt cover an area of approximately 234.82 sq.km (8.99%), 370.21 sq.km (14.17%), 141.23 sq.km (5.40%), 75 sq.km (2.87%) and 20.66 sq.km (0.79%), respectively.The loamy and clay loam soils were given the highest rank owing to their water holding capacity, fertility, and nutrient enrichment, while loamy sand and silt were given the lowest rank due to their relatively low water holding capacity, fertility, and nutrient content.

Soil organic carbon
The amount of carbon stored in soil organic matter is known as soil organic carbon (SOC).It plays a crucial role in controlling temperature, biomass production, water holding capacity, nutrient cycling, and soil fertility on Earth.Previous research has revealed that grassland soil carbon stores have been largely reduced worldwide as a result of improper usage or management but that management changes may also boost soil carbon stocks and prevent the deterioration of grasslands 76 .A thorough analysis revealed that the SOC content in the region's soils ranged from 0.12 to 1.24 gm/kg (Fig. 6A), with the bulk decreasing between 0.  www.nature.com/scientificreports/

EC
The electrical conductivity (EC) of a solution is a measure of its salt content and is an indicator of its electrolyte concentration.The number of ions available to plants in the root zone corresponds to the EC of the nutrient solution 74 .A higher EC typically lowers nutrient absorption by increasing nutrient solution osmotic pressure, wastes nutrients, and increases nutrient outflow into the environment, resulting in environmental pollution.A lower EC may have adverse effects on plant health and output.Most crops have an EC range of 2 to 3.5 mS/cm.Previous research has revealed that soil salinisation is linked to the degradation of semiarid and arid grasslands.
The degradation of grasslands has also accelerated as a result of a negative feedback mechanism caused by changes in land cover and significant salinisation of the soil 77 .In the studied area, the EC varied from 0.11 to 76.79 dS/m (Fig. 7B), with the bulk decreasing between 0.11 and 6.63 dS/m and encompassing 2031.89sq.km (or 77.7%) of the land.A total of 18.9 sq.km (0.72%) are covered by an area with very high salinity in the range of approximately 47. 88

Precipitation
The primary water source in the hydrological cycle and the most important determining factor for a region's groundwater is rainfall 40 .Precipitation timing and amount are the primary factors that most significantly influence the health of semiarid grassland ecosystems.In a semiarid grassland, the amount of green plants changes greatly from year to year in part because of precipitation 78 .The 2021 annual rainfall data from the IMD were utilised in this study.The yearly rainfall fluctuates between 1180 and 2157 mm (Fig. 6B).The rainfall was divided into three groups based on the maximum and minimum values: low (594.

Water holding capacity (WHC)
In semiarid grassland ecosystems, the amount of water in the soil is the main constraint on plant development and carbon cycling 79 .Monitoring water holding capacity is critical for successful water management in horticulture and agriculture.It helps determine the soil's ability to hold water by providing crucial information for irrigation scheduling, water conservation, and other related tasks.By frequently monitoring water retention capacity, farmers and growers may reduce water waste and adverse environmental effects while improving irrigation practices, preventing waterlogging or drought stress, and increasing crop production 80 .The water holding capacity of the research region ranges from 14.82 to 48.41% (Fig. 6C).The low water holding capacity covers an area of approximately 243.19 sq.km (9.3% of the total area).Similarly, medium, high, and very high water holding capacities cover areas of approximately 783.56 (29.9% of the area), 1267.68 (48.5% of the area), and 317.44 sq.km (12.1% of the area), respectively.

Infiltration rate
Soil water infiltration is the process by which irrigation or rainfall penetrates the soil either horizontally or vertically through pores, tying surface water, soil water, and groundwater together.Plants can only take up and use soil water through soil infiltration 81 .In dry and semiarid climates, this process is the primary impediment to the establishment of grasslands and ecological restoration.It is closely linked to soil erosion, runoff, and nutrient migration 82 .Rainfall duration and intensity both affect infiltration.High-intensity and short-duration rain causes less infiltration and more surface runoff, whereas low-intensity and long-duration rain results in more infiltration than does run-off 40 .The infiltration rate in the research region varied from 2.60 to 62.99 (Fig. 6D).A region of approximately 502.84 sq.km is covered by a low infiltration rate (2.60 to 22.73 min).Similarly, medium

Land use land cover (LULC)
Satellite imagery was used to better understand the specifics of land use and land cover classification (LULC), which is crucial for identifying potential land suitability restoration points of interest.LULC provides vital information on the infiltration rate, moisture in the soil, groundwater, surface water, and so on, as well as information on grassland requirements 40,83 .The land use/land cover map was classified into eight categories: built-up areas, water bodies, barren land, agricultural areas, salt-affected areas, dense grassland, sparse grassland, and barren land (Fig. 6E).As a result, 356.46

Geomorphology
Environmental management depends heavily on geomorphology or the study of landforms and the physical processes that shape them.Geomorphological units, or the physical properties of the Earth's surface and nearsurface, are crucial for hydrogeological studies, topographical analysis, and the identification of groundwater resources 84 .Because geomorphological processes are a component of a larger system of interconnected phenomena, it is important to consider the social, economic, and cultural context of the area when evaluating their environmental importance 63 6F).

Slope
In land management studies, the slope characteristics of a place are particularly important 63 .This classification assists in highlighting the primary limiting criteria for certain applications, such as plantations.Gradient limitations are particularly significant in transportation and agriculture.Because of stochastic local extinction and colonisation events, successional change or a response to changing management, the species composition of fragmented semi natural grasslands may change over time.The influences of topography (slope and aspect) on soils and the microclimate may mediate vegetation resilience to change 85 .The Banni grassland's slope map is shown in Fig. 8.The slope values were classed and divided into flat (0-3°), gentle (3°-5°), and steep (> 5°) classes (Fig. 6G).A high weight is assigned for flat and gentle slopes.A low weight is assigned for steep slopes.

Bulk density
Soil bulk density is a fundamental yet important physical soil characteristic related to soil porosity, soil moisture, and hydraulic conductivity for the purpose of evaluating soil quality and controlling land use.Because soil bulk density varies with spatial scale and layer, complex processes of soil formation, ecological conditions, and human activities all contribute to the geographical variety of soil bulk density.Thus, knowledge of the factors influencing soil bulk density and geographical distribution can be helpful in predicting relevant soil processes and enhancing soil quality 86 .The concentration of organic matter, soil management practices, and soil particle sizes all affect the optimal and critical limits of soil BD.A bulk density of less than or equal to 1.3 g cm −3 is good, that between 1.3 and 1.55 g cm −3 is fair, and that greater than 1.8 g cm −3 is considered extremely poor 87 .The bulk density ranged from 1.21 to 1.83, with the majority falling between 1. 21

SAR
When assessing a soil's suitability for agricultural uses, particularly irrigation, the SAR of the soil must be taken into consideration.The SAR calculates the soil sodium content in relation to other cations (such as calcium and magnesium).High SAR values suggest the possibility of soil sodicity, which can result in poor soil structure, decreased water infiltration, and impaired plant development.Farmers may prevent soil deterioration, increase crop yield, and improve water quality by routinely measuring SAR.A suitable agricultural soil produces SAR values lower than 13 89 .The SAR ranged from 0.01 to 1.63; however, the majority ranged between 0.01 and 0.55 and covered 1423.43 sq.km (or 54.4%) of the land (Fig. 7A).A total of 348.02 sq.km (13.32%) and 840.51 sq.km (32.1%) are in the range of 0.55 to 1.09 and 1.09 to 163, respectively.

Grassland suitability analysis (GSA)
A combined suitability map was generated via weighted overlay analysis utilising the criterion layers with their respective weights.The available N, available P, available K, Fe, Mn, Zn, Cu, pH, SOC, soil texture, EC, rainfall, WHC, infiltration rate, LULC, geomorphology, slope, bulk density, CEC and SAR thematic reclassified polygon maps were then superimposed on this suitability map to determine the final suitability map.According to this map (Fig. 8

Validation of the grassland suitability map
The dense grassland was shown to be related to a flat slope, good soil quality, and the availability of major and minor nutrients, whereas the sparse grassland was found to be associated with a gentle slope, moderate soil quality, and low to moderate levels of soil nutrients.Moreover, low to moderate soil characteristics and soil nutrients were also connected with barren land and other LULC classifications.The validation factors of grassland suitability and development were therefore based on the thematic layers of the examined region as determined by a field study.By comparing the classified data (output) with the data from the training set, the accuracy was evaluated.Using cross-tabulation of classified data against reference data, an error matrix was obtained.When classified data are compared to reference data, their accuracy is evaluated.The cross-tabulation of categorised data against reference data yields an error matrix.The accuracy of users, producers, and overall accuracy were determined using an error matrix.In addition to determining accuracy levels, the accuracy assessment technique was also used to improve accuracy levels.Ground reference locations were chosen from the research area using the GPS technique, and they were validated using classified data.Through the use of a GPS survey, 168 ground reference points from the research region were confirmed, and they were then compared with classified data.The overall accuracy of the classified map was calculated to be 85.22% (Table 7).

Conclusion
The aim of the current study was to determine the grassland development zones in the flat, undulating Banni grassland in western India by combining soil sampling analysis and remote sensing and GIS techniques.In this study, the delineation of grassland restoration zones was performed using a total of 20 thematic layers, including available nitrogen, available phosphorus, available potassium, Fe, Mn, Zn, Cu, pH, soil texture, soil organic carbon, EC, rainfall, water holding capacity, infiltration rate, land use land cover, geomorphology, slope, bulk density, sodium adsorption ratio, and cation exchange capacity.To generate land suitability analysis maps for grassland suitability, the MCDM tool with GIS was utilised.The impact of certain criteria was determined by expert analysis and views, which were then estimated using MCDM and the AHP, which was used to assign weights.According to our findings on grassland patterns and suitability, highly suitable land (937 sq.km) and suitable land (728 sq.km) have no significant restrictions if water sources are provided either through irrigation or rainwater harvesting.Grassland expansion is also feasible in somewhat appropriate areas (moderately suitable-714 sq.km), although careful land management is needed.For grassland development, there is marginally suitable land (182 sq.km) and not suitable land (61 sq.km), necessitating terracing, supplementary inputs such as fertilisers, protection from high runoff and erosion, salt intrusion, etc.As a consequence, decisions may be made knowing exactly how they will affect future land use development and how they will affect the impacts.Therefore, it is essential to assess grassland degradation thoroughly along with climate, topography, vegetation, and soil characteristics and use various techniques for analysis, such as remote sensing, to systematically analyse the suitability of grassland development, thereby improving the science and accuracy of the research.Since degraded grasslands make up the majority of the study region, this research will enhance the area's grassland productivity and fertility database with the aid of required inputs.The Banni grassland of western India was once considered the finest grassland of Asia; however, owing to various natural and anthropogenic factors, it is now on the verge of drastic degradation.In this work, we incorporated edaphic and proximal GIS-based factors to provide a concise view, taking into consideration all necessary factors required for the sustenance of a grassland ecosystem.The Banni grassland is an ecosystem for which ecological vulnerabilities need to be considered while considering geo-ecological perspectives.The findings of this study have significant implications for the conservation and sustainable use of grasslands in western India.By identifying areas with high ecological value and suitability, this research provides valuable guidance for policymakers, land managers, and local communities in making informed decisions regarding land management practices.The findings can help generate evidence-based recommendations for sustainable grassland management, including biodiversity conservation and the enhancement of livelihoods for local communities dependent on grasses for their livestock.Furthermore, the study's methodology and outcomes can be replicated and applied to similar grassland ecosystems in other regions.This research contributes to the broader understanding of grassland management and restoration practices, enabling the transfer of knowledge and best practices to other areas facing similar ecological challenges.Overall, this study underscores the importance of assessing land suitability for sustainable grassland management and highlights the

Table 1 .
Data utilised in the research and their sources.

Table 2 .
Pairwise comparison matrix table of twenty thematic layers chosen for the present study.

Table 3 .
Categorisation of factors influencing grassland suitability and the ranks and weights of thematic layers.

Table 4 .
Results of the consistency ratio.

Table 5 .
Saaty 's ratio index for different values of N.The consistency
and 10.38 and covering 1086.52 sq.km (or 41.5%) of the land.A region with low Mn in the range of approximately 10.38 to 18.49 ppm covers 853.91 sq.km (32.6%) of the total area.Similarly, high Mn covers an area of approximately 113.81 sq.km (4.3%), and medium Mn covers an area of approximately 558.03 sq.km (21.3%), respectively.High Mn contents range from 26.60 to 34.71 ppm.
sq. km (18.05%) of the study region is made up of dense grassland.Sparse grassland falls under the category of substantial land cover and makes up 887.31 sq.km (33.96%) of the research area.Land use/land cover under water bodies, barren land, agriculturally productive regions, salt-affected areas, . The geographical region's geomorphology includes high-level mudflats, which cover approximately 344.23 sq.km (13.17%) of the land; low-level mudflats with saline depressions, which cover approximately 583.47 sq.km (22.33%); lineaments; and shallow gullies, as well as mid-level mudflats, which cover approximately 1685.14 sq.km (64.49%) of the area (Fig.
Approximately 2518.41 sq.km. of the region are covered by flat land, 73.38 sq.km. by gentle slopes, and just 6.94 sq.km. by steep slopes.
88e cation exchange capacity (CEC) is a critical parameter for assessing the fertility and nutrient-holding capacity of soil.It measures the soil's ability to retain and exchange cations, such as potassium, calcium, magnesium, and other essential nutrients, with the soil solution.A high CEC indicates that the soil can hold more nutrients, reducing the risk of leaching and nutrient loss88.By understanding the CEC of soil, farmers and agronomists can effectively manage fertiliser application, tailor nutrient management strategies, and maintain optimal soil fertility for healthy plant growth and sustainable agricultural practices.The CEC ranged from 17.26 to 145.69 meq/100 g (Fig.6I), with the majority ranging between 17.26and 60.07 meq/100 g and covering 1815.07 sq.km (or 69.4%) of the land.A total of 743.74 sq.km (28.4%) have values ranging from 60.07 to 102.88 meq/100 g, whereas 53.76 sq.km (20.05%) have values ranging from 102.88 to 145.69 meq/100 g. CEC

Table 6 .
),35.71% (937 sq.km) of the study area is highly suitable, 27.77% (728 sq.km) is suitable, 27.23% (714 sq.km) is moderately suitable, and 6.94% (182 sq.km) is marginally suitable.Approximately 2.34% (61 sq.km) is determined to be 'not suitable' at all.Table6presents the details of the area and land characteristics of the different suitability categories for Banni grassland.Land suitability classes and their characteristics.
Generally, gentle slopes (< 5°), pH moderate to high, Fair to low soil nutrients, low SOC, low to mostly medium bulk density, medium to high WHC, low to medium SAR Medium favourable for the development of grasslands under careful land management.Although it is feasible, protecting the soil from severe erosion and salt intrusion is necessary through interventions Not suitable 61 sq.km Generally, gentle slopes (< 5°), pH high to very high, low soil nutrients, low to very low SOC, low bulk density, high to very high EC, medium to high SAR, low to medium WHC Grasslands and planting is not possible on these regions due to geological and environmental controls

Table 7 .
The error matrix for grassland suitability and ground data.